One-year-later spontaneous EEG features predict visual exploratory human phenotypes

During visual exploration, eye movements are controlled by multiple stimulus- and goal-driven factors. We recently showed that the dynamics of eye movements –how/when the eye move– during natural scenes’ free viewing were similar across individuals and identified two viewing styles: static and dynamic, characterized respectively by longer or shorter fixations. Interestingly, these styles could be revealed at rest, in the absence of any visual stimulus. This result supports a role of intrinsic activity in eye movement dynamics. Here we hypothesize that these two viewing styles correspond to different spontaneous patterns of brain activity. One year after the behavioural experiments, static and dynamic viewers were called back to the lab to record high density EEG activity during eyes open and eyes closed. Static viewers show higher cortical inhibition, slower individual alpha frequency peak, and longer memory of alpha oscillations. The opposite holds for dynamic viewers. We conclude that some properties of spontaneous activity predict exploratory eye movement dynamics during free viewing.


Supplementary Methods
To assess LRTCs, we applied a Detrended Fluctuation Analysis on both EEG sensor data and eyetracking data. Detrended Fluctuation Analysis is a technique to assess long-range temporal correlations in non-stationary signals ( 2 ). This technique extracts a power-law exponent, typically ranging between 0.5 and 1 in brain signals ( 3 ) and eye movements time-series ( 4 ). While an exponent of 0.5 index an uncorrelated signal (i.e., white noise), an exponent of 1 index strong longrange temporal correlations ( 2,5 ).
Pre-processed eye-tracking data were transformed into a time-series comparable to those extracted from the EEG signal (cfr. 6 ).
For this step, we considered the fixed viewing time (2s) and excluded the rest of the exploration time per image. Images containing missing data for at least one subject are excluded, resulting in a final pool of 128 images and a total number of 31200 time points.
Fixations were extracted with the fixation detection algorithm implemented in R in the package 'saccades' (https://github.com/tmalsburg/saccades), in which the detection is obtained using a velocity-based algorithm for saccade detection proposed by Engbert and Kliegl ( 7 ). Anything between two saccades is considered a fixation.
A time-series was created, in which every time point is represented (120 frames per second). Timepoints in which a fixation was detected in the previous step were assigned a zero value, while all the other points, which represent eye movements, were assigned value one.
High-density EEG data received an additional pre-processing (cfr. 6 ) before the DFA: first, the EEG signals were bandpass filtered in the frequency of interest (7.5-12 Hz, order 66), and then the amplitude envelope was extracted using a Hilbert transform. We choose the alpha band as the frequency of interest for this step because, based on the existing literature (e.g., 6 ) this is the frequency band showing the strongest association with behavioural data, both at rest and during task.
To avoid spurious temporal correlations induced by the filter, the filter order and the lowest fitting window were chosen based on a simulation. We simulated 1000 white Gaussian noises with the same length of the signal and applied the filter. Filter order was chosen following Hardstone et al. 5 as two cycles of the lower bound of the frequency of interest (2 cycles of 7.5 Hz); while the lowest fitting window was chosen as the one where DFA exponents deviate from the expected known value (i.e., 0.5) in the white Gaussian noise simulation. The resulting lowest fitting window is 2.36 seconds (590 time points). For the 2.36-64 s intervals, the scaling exponents obtained for the white Gaussian noise simulation with a FIR pass-band filter (cut-off frequencies: 7.5-12, order: 66) had a mean value of 0.508 ±0.025. The expected DFA exponent for a white Gaussian noise is 0.50. For the high end of the fitting window, the maximum length allowed has to be at most N/4, where N is the total number of time points ( 5 ); this is because the number of segments in the averaging procedure would become otherwise too small and thus statistically unreliable. Therefore, the highest fitting window was determined as N/4 time points, as computed from the shortest signal (i.e. behavioural data), resulting in a maximum fitting window of 64 sec.
Next, both time-series are integrated (i.e., the mean-centred cumulative sum is computed): (1) Where a(t) is the value of the time series at point t and <a> is the mean of the time series.
The signal is then split into 50 logarithmically spaced time-windows varying from 2.36 to 64 sec. Each segment of the integrated data is locally fitted to a linear function and the mean-squared residual is computed: where N is the total number of data points. The scaling exponent is defined as the slope of the linear regression of the function in log-log coordinates, estimated using a least-squares algorithm.